Experience of the bodily self is dependent upon the integration of current sensory signals with existing knowledge and prior expectations about the body. As such, the way in which the self is perceived is not a direct reflection of the body’s actual state, but rather an interpretation of available sensory information. The phenomena of “medically unexplained symptoms” provide an illustration of this, whereby individuals experience subjectively real somatic symptoms despite the absence of any organic cause. The work in this thesis aimed to investigate how individuals experiencing somatic symptoms process multisensory information about the body and, more specifically, set out to test the hypothesis that symptom reporting is associated with a general tendency to over-weight top-down information during the process of body representation. The role of visual information in shaping bodily perception was explored by using visual manipulations that distorted the appearance of the body, introducing a discrepancy between sensory information and top-down knowledge about the body. The findings show that visual information can have a significant effect on the way in which the body is perceived, and also demonstrate that perception of the self arises from a dynamic interaction between top-down and bottom-up inputs (Chapters 2 and 3). Contrary to hypotheses, the results do not support suggestions that somatic symptom experience is characterised by disturbances in body representation; individuals who report a higher number of somatic symptoms were found to process multisensory information about the body in a comparable way to those who reported few symptoms, demonstrating that somatic symptom experience is not associated with abnormal processing of visual, spatial and temporal sensory information about the body (Chapters 4 and 5). Furthermore, there is no evidence to suggest that high symptom reporters rely more on top-down information during the process of body representation, even in the presence of strong contextual cues (Chapters 5 and 6). The final study also observed no relationship between somatic symptom reporting and interoceptive accuracy, indicating that high symptom reporters perceive and integrate both exteroceptive and interoceptive sensory information in a typical fashion (Chapter 6). Overall, this work uses experimental methods to explore a number of theoretical concepts proposed as relevant for unexplained symptoms, concluding that somatic symptom experience amongst the general population is not characterised by abnormal multisensory integration or disrupted body representation.

Specific locations within the brain contain neurons which respond, by firing action potentials (spikes), when a sound is played in the ear of a person or animal. The number and timing of these spikes encodes information about the sound; this code is the basis for us perceiving and understanding the acoustic world around us. To understand how the brain processes sound, we must understand this code. The difficulty then lies in evaluating the unknown neural code. This thesis applies Machine Learning to evaluate auditory coding of dynamic sounds by spike trains, with datasets of varying complexity. In the first part, a battery of Machine Learning (ML) algorithms are used to evaluate modulation frequency coding from the neural response to amplitude-modulated sinusoids in cat Cochlear Nucleus spike train data. It is found on this recognition task that, whilst absolute performance levels depend on the types of algorithms, their performance relative to each other is the same on different types of neurons. Thus a single powerful classification algorithm is sufficient for evaluating neural codes. Similarly, different performance measures are useful in understanding differences between ML algorithms, but they shed little light on different neural coding strategies. In contrast, the features used for classification are crucial; e.g. Vector Strength does not provide an accurate measure of the information contained in spike timing. Overall, different types of neurons do not encode the same amount of amplitude-modulation information. This emphasises the value of using powerful Machine Learning methods applied to raw spike timing information. In the second part, a more ecological and heterogeneous set of sounds — speech — is used. The application of Hidden Markov Model based Automatic Speech Recognition (ASR) is tested within the constraints of an electrophysiological experiment. The findings suggest that a continuous digit recognition task is amenable to a physiology experiment: using only 10 minutes of simulated recording to train statistical models of phonemes, an accuracy of 70% could be achieved. This result jumps to about 85% when using 200 minutes worth of simulated data. Using a digit recognition framework is sufficient to examine the influence on the performance of different aspects of the size and nature of a neural population and the role of spike timing. Previous results suggest, however, that this accuracy would be reduced if experimental Inferior Colliculus data were used instead of a guinea-pig cochlear model. On the other hand, a fully-fledged continuous ASR task on a large vocabulary with many speakers may result in insufficient phoneme accuracy (∼40%) to base an auditory coding-related investigation on. Overall this suggests that complex ML algorithms such as ASR can nevertheless be practically used to assess neural coding of speech, with careful selection of features.

The mechanisms involved in the cause and progression of chronic neurodegenerative diseases are still unclear. The ubiquitin proteasome system (UPS) plays an essential role in the maintenance of intracellular protein homeostasis by degrading unwanted proteins. The accumulation of ubiquitinated proteins is a hallmark of major neurodegenerative diseases, including Alzheimer’s and Parkinson’s diseases. In most cases, these diseases are also associated with changes in cytoskeletal proteins and microtubule stability. We previously reported decreased levels of microtubule destabilizing protein stathmin (STMN) following 26S proteasome dysfunction in mouse cortical neurons; associated with neurodegeneration and the formation of intraneuronal protein inclusions in surviving neurons. This suggested a role for the 26S proteasome in maintaining the neuronal cytoskeleton. This thesis investigates the levels and localisation of cytoskeletal proteins in mouse cortical neurons following 26S proteasome dysfunction (Psmc1fl/fl;CaMKIIα-Cre). This study provides new insights into the role of the UPS in maintain cytoskeletal proteins that may be important in neurodegenerative disease. We found an early increase in neurofilaments following 26S proteasome dysfunction; before obvious changes in microtubule stability. An increased free/polymerised tubulin ratio was evident at later stages indicative of microtubule instability in Psmc1fl/fl;CaMKIIα-Cre mice. In addition, we found decreased levels of microtubule proteins, microtubule-associated proteins and detyrosinated-tubulin; with increased tyrosinated-tubulin following 26S proteasome dysfunction. These changes are contrary to decreased STMN expression observed in Psmc1fl/fl;CaMKIIα-Cre mice. We suggest that decreasing STMN may be part of a negative feedback loop to stabilize MT following 26S proteasome dysfunction. STMN is known to be a downstream target of p53, p27 and the PI3K/Akt pathway. We found expression of p53 was increased in the cortex following 26S proteasome dysfunction, correlating with decreased phosphorylated-Akt expression at an early stage and may effect STMN expression. However, we did not observe any significant differences in pro- and anti-apoptotic proteins of the Bcl-2 family between control and Psmc1fl/fl;CaMKIIα-Cre mice, which may also by effected by p53 and phosphorylated-Akt. Immunohistochemical studies revealed changes in cortical neuron morphology accompanied 26S proteasome dysfunction. Cortical thickness was significantly decreased; associated with less neurons in the layers III and V. However, nuclear size of cortical neurons was increased, as well as the length and arborisation of their apical dendrites. Taken together, our novel data contributes to our understanding of molecular and cellular events underlying neurodegeneration and suggest that control of microtubule changes may help to slow or restore pathology of neurons.

Diet plays a pivotal role in preventing, managing, and reducing the risk of weight gain, diabetes and heart disease. Increasing pressure is directed towards food manufacturers to offer healthier options. The challenge is to develop products which are both nutritious and accepted by the consumer. Oral sensitivity, and therefore product perception, varies greatly amongst individuals, and likely affects food choice. Taste phenotype and genotype are frequently proposed as markers for overall oral sensitivity. This thesis performs fundamental research to further current understanding of the impact of taste phenotype and genotype on the response to oral stimuli. The effect of 6-n-propylthiouracil (PROP) taster status (PTS), fungiform papillae density, TAS2R38 and gustin rs2274333 genotype on the perceived intensity of prototypical tastants and metallic stimuli is explored. Experiments were first conducted to develop oral stimuli for use in the subsequent fMRI studies, which interestingly identified that some metallic stimuli may have a gustatory component. Perceptually, few or no differences were identified across taste phenotypes or genotypes. Interestingly, functional Magnetic Resonance Imaging (fMRI) identified variation in cortical processing that was associated with PTS. PROP intensity ratings were found to correlate with cortical activation in the anterior insula, an area of the brain thought to be the primary gustatory cortex, in response to sweet and metallic stimuli, but not for sour, salt, bitter or umami stimuli. These limited differences observed may have been due to the occurrence of a concentration effect, where the increased gustatory sensitivity frequently associated with PROP tasters compared to PROP non-tasters was lost when administering strong supra-threshold stimuli used in the current study. These findings are of interest to food manufacturers and health professionals as they could indicate that taste phenotype and genotype has less impact on product perception, and therefore food choice, than previously proposed. Thermal taster status (TTS) refers to a new taste phenotype in which individuals perceive phantom tastes when the tongue is thermally stimulated, whilst thermal non-tasters (TnTs) only perceive temperature. In this thesis, variation in the phantom tastes reported by thermal tasters (TTs) are explored, and for the first time the temporal phantom taste response is measured. Different categories of temporal taste responses are identified, and interestingly it is shown that phantom tastes are perceived at variable temperature ranges across both TTs and taste qualities. Importantly, the onset of sweet taste was found to occur as the temperature increased between 22-35°C, supporting the hypothesis that the TRPM5 may be involved in sweet phantom taste responses. This is the first study to assess the brain’s response when thermally stimulating the tongue of TTs to elicit a phantom taste response. Interestingly, when using fMRI it is shown that at the time when TTs perceive a phantom taste, cortical activation is induced in the anterior insula, which is thought to be the primary gustatory cortex. This indicates that thermal stimulation may activate temperature sensitive gustatory nerve fibres in TTs, and supports the hypothesis of cross wiring between gustatory and trigeminal nerves. When comparing the cortical response to thermal stimulation of the tongue across TTs and TnTs, greater activation is observed in oral somatosensory areas of the brain in TTs compared to TnTs. These findings show cortical processing differs across thermal taste phenotype, and supports evidence that thermal taster status may be a marker for oral sensitivity. This original research provides a valuable contribution towards understanding the effect of taste phenotype and genotype on perception of prototypical taste, metallic, and thermal stimuli. The novel multidisciplinary approach of utilising sensory evaluation and fMRI techniques has provided valuable insights into the impact of taste phenotype on gustatory responses, and has suggested possible mechanisms that may be involved in thermal taste phenotype.

Language has been the subject of academic fascination for centuries, and the ability to communicate abstract notions through speech and writing allows humans to interact in ways that would not otherwise be possible. While the mechanisms of language processing have been studied extensively with behavioral and noninvasive neuroimaging methods, much about how the brain encodes language remains unknown. In this dissertation, I describe experiments using intracranial neurophysiology in humans to interrogate the mechanisms of language perception at high spatiotemporal resolution. First, I explore the neural mechanisms of visual word recognition in a large human intracranial dataset. By analyzing population sensitivity to a hierarchy of word features, I create a high-resolution map of stimulus encoding during single-word reading that reveals the early influence of lexical features in lingual and fusiform gyri followed by a cascade of lexical, orthographic, and semantic information in temporal and frontal lobes. Along with clustering analyses that show stimulus encoding in anatomically distributed populations, these results demonstrate that feed-forward, feed-back, and distributed processing mechanisms underlie visual word recognition. Second, I describe the development of an artificial language task designed to characterize the neural mechanisms of auditory word segmentation. The task is designed in three phases to probe how the brain tracks distributional regularity and the neural mechanisms of word segmentation with and without lexical access. Taken together, this work expands our understanding of the neural mechanisms of language processing using human intracranial neurophysiology.

The aim of this study was to further examine the relationship between anxiety, inhibitory control (IC), and brain functioning (electroencephalogram) in a critical age-range for social and emotional development (8-12-year-olds). Depression was a secondary focus but was included in the analyses given the common anxiety/depression overlap. Additionally, the participants (N = 42) were assigned to 4 weeks of either an emotional training program (Emotional gFocus), a neutral training program (Neutral gFocus), or a waitlisted control and were tested using cognitive, neurophysiological, and mood measures. Hierarchical regression models revealed that IC accuracy scores were significantly and negatively related to anxiety levels as indicated by the Screening For Child Anxiety Related Disorders (SCARED), as well as depression levels (using the Child Depression Inventory (CDI)), controlling for age and gender. Additionally, increased resting-state right lateral frontal alpha asymmetry was predictive of increased anxiety as well as depression levels. To evaluate the intervention effects, a series of Multivariate Analyses of Covariance (MANCOVA) and contrast tests were conducted to determine if group differences existed from pre-to-post for any of the measures of interest. Overall, the emotional and neutral training conditions showed similar reductions in anxiety and depression compared to the waitlist condition. Both the emotional and neutral conditions also facilitated significant improvements in IC accuracy compared to the control. Minimal pre-to-post power and asymmetry changes occurred in frontal and parietal regions; however, a lateral frontal leftward activity shift was found in the emotional training group. These findings further demonstrated a relationship between IC and anxiety and showed preliminary evidence that training IC has the potential to mitigate negative emotional functioning in adolescents. Future research is necessary to determine the importance of emotional training versus neutral as well as whether longer training intervals will be needed to facilitate a long-term impact. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Based on evidence of abnormalities in axon thickness and neuronal disorganization, autism spectrum disorder (ASD) is commonly considered to be a condition resulting from neuronal dysfunction. Yet, recent findings suggest that non-neuronal cell types also contribute to ASD pathology. To investigate the role of glial cells in ASD, a combination of protein and gene expression analyses were used to determine levels of two glial markers, glial fibrillary acidic protein (GFAP) and myelin oligodendrocyte glycoprotein (MOG), in the postmortem brain tissue from control and ASD donors. Levels of GFAP immunoreactivity (ir) were significantly elevated (P = 0.008) in anterior cingulate cortex (Brodmann area 24; BA24) white matter of ASD donors compared to control donors. In contrast, GFAP-ir levels were similar in BA24 gray matter from ASD and control donors. MOG-ir was also similar in both BA24 white and gray matter from ASD and control donors. In anterior prefrontal cortex (BA10), there were no significant differences in GFAP-ir or MOG-ir in either white or gray matter comparing ASD to control donors. Levels of expression of the genes GFAP and MOG also showed no differences between control and ASD donors in BA24 and BA10 white and gray matter. Collectively, these data imply that ASD is associated with an activation of white matter astrocytes in the anterior cingulate cortex as a result of a yet undefined cellular insult. Research is needed to investigate the molecular pathways that underlie this astrocyte reaction and such research may yield important clues regarding the etiology of ASD.

An important question in sensory neuroscience is what coding strategies and mechanisms are used by the brain to detect and discriminate among behaviorally relevant stimuli. To address the noisy response properties of individual neurons, sensory systems often utilize broadly tuned neurons with overlapping receptive fields at the system's periphery, resulting in homogeneous responses among neighboring populations of neurons. It has been hypothesized that progressive response heterogeneity in ascending sensory pathways is evidence of an efficient encoding strategy that minimizes the redundancy of the peripheral neural code and maximizes information throughput for higher level processing. This hypothesis has been partly supported by the documentation of neural heterogeneity in various cortical structures. This dissertation examines whether selective and sensitive responses to behaviorally relevant stimuli contribute to a heterogeneous and efficient encoding in the auditory midbrain. Prior to this study, no compelling experimental framework existed to address this question. Stimulus design methodologies for neuroethological experiments were largely based on token vocalizations or simple approximations of vocalization components. This dissertation describes a novel state-space signal modeling methodology which makes possible the independent manipulation of the frequency, amplitude, duration, and harmonic structure of vocalization stimuli. This methodology was used to analyze four mouse vocalizations and create a suite of perturbed variants of each of these vocalizations. Responses of neurons in the mouse inferrior colliculus (IC) to the natural vocalizations and their perturbations were characterized using measures of both spike rate and spike timing. In order to compare these responses to those of peripheral auditory neurons, a data-driven model was developed and fit to each IC neuron based on the neuron's pure tone responses. These models were then used to approximate how peripheral auditory neurons would respond to our suite of vocalization stimuli. Using information theoretic measures, this dissertation argues that selectivity and sensitivity by individual neurons results in heterogeneous population responses in the IC and contributes to the efficient encoding of behaviorally relevant vocalizations.

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